In this paper, we propose automatic energy leakage detection in underground pipes of district heating systems based on Infrared (IR) images, captured by an Unmanned Aerial Vehicle (UAV). Hot water or steam is distributed to homes and industries through underground pipes from a central power plan. Leakages in underground pipes pose a very common problem, which can occur for many reasons, e.g. unprofessional installation and end of service life. Potentially, a leakage remains undiscovered for a very long period of time. Therefore, it is of great interest for power supply companies to monitor district heating networks to identify leakages. In this paper, the original IR images are captured in a 16 bit format by a UAV. On ground, potential leakages are extracted using a region extraction algorithm. Thereafter a Convolutional Neural Network (CNN) as well as eight conventional Machine Learning (ML) classifiers are applied on these regions to classify whether or not it is a leakage.

In total, twelve UAV sequences are captured at different cities in Denmark. Based on these, around 13.4 million samples of image patches of district heating systems are extracted. Eleven sequences are used for training and the remaining one for testing. This was performed on all splits in the leave-one-out testing. The deep learning CNN achieved an average weighted accuracy of 0.872 with a false positive and negative rate of 12.7 % and 10.4 %, respectively. This CNN model detected around 98.6 % of the true leakages. In comparison, conventional ML classifiers, i.e. Adaboost (AB), Random Forest (RF), etc. provide lower average weighted accuracy, but on the other hand they require less computational resources. We have compared our method with a state-of-art method and the result shows that the proposed method is very competitive.

在本文中，我们建议基于无人飞行器（UAV）捕获的红外（IR）图像，对区域供热系统的地下管道进行自动能量泄漏检测。热水或蒸汽通过中央电力计划通过地下管道分配给家庭和工业。地下管道的泄漏是一个非常普遍的问题，可能由于许多原因而发生，例如，专业安装和使用寿命终止。潜在地，泄漏会在很长一段时间内未被发现。因此，对于供电公司来说，监视区域供热网络以识别泄漏十分重要。在本文中，原始红外图像是由无人机以16位格式捕获的。在地面上，使用区域提取算法提取潜在的泄漏。

总共在丹麦的不同城市捕获了12个无人机序列。基于这些，提取了大约1,340万个区域供热系统的图像样本。使用11个序列进行训练，其余1个序列进行测试。这是在留一法测试中对所有拆分执行的。深度学习CNN的平均加权准确度为0.872，假阳性率和阴性率分别为12.7％和10.4％。该CNN模型检测到约98.6％的真实泄漏。相比之下，常规的ML分类器，即Adaboost（AB），Random Forest（RF）等提供较低的平均加权精度，但另一方面，它们需要较少的计算资源。我们将我们的方法与最先进的方法进行了比较，结果表明该方法具有很好的竞争力。